Допустим я хочу научить распознавать символ на изображении, в каком формате мне изображение на вход сети дать и как на выходе ей результат правильный представить
pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ]
примерно вроде понимаю, начинаю проверять, не пойму , запутался…
вот тренировался на этой библиотечке
import math import random import string random.seed(0) # calculate a random number where: a <= rand < b def rand(a, b): return (b-a)*random.random() + a # Make a matrix (we could use NumPy to speed this up) def makeMatrix(I, J, fill=0.0): m = [] for i in range(I): m.append([fill]*J) return m # our sigmoid function, tanh is a little nicer than the standard 1/(1+e^-x) def sigmoid(x): return math.tanh(x) # derivative of our sigmoid function, in terms of the output (i.e. y) def dsigmoid(y): return 1.0 - y**2 class NN: def __init__(self, ni, nh, no): # number of input, hidden, and output nodes self.ni = ni + 1 # +1 for bias node self.nh = nh self.no = no # activations for nodes self.ai = [1.0]*self.ni self.ah = [1.0]*self.nh self.ao = [1.0]*self.no # create weights self.wi = makeMatrix(self.ni, self.nh) self.wo = makeMatrix(self.nh, self.no) # set them to random vaules for i in range(self.ni): for j in range(self.nh): self.wi[i][j] = rand(-0.2, 0.2) for j in range(self.nh): for k in range(self.no): self.wo[j][k] = rand(-2.0, 2.0) # last change in weights for momentum self.ci = makeMatrix(self.ni, self.nh) self.co = makeMatrix(self.nh, self.no) def save(self, fname): f = open(fname, "w") f.write(str(self.ni)+";") f.write(str(self.nh)+";") f.write(str(self.no)+";") for i in xrange(self.ni): for j in xrange(self.nh): f.write(str(self.wi[i][j])+";") for i in xrange(self.nh): for j in xrange(self.no): f.write(str(self.wo[i][j])+";") f.close() def load(self, fname): f = open(fname, "r") line = f.read() f.close() arr = string.split(line, ";") self.ni, self.nh, self.no = int(arr[0]), int(arr[1]), int(arr[2]) self.wi = makeMatrix(self.ni, self.nh) self.wo = makeMatrix(self.nh, self.no) n = 2 for i in xrange(self.ni): for j in xrange(self.nh): n += 1 self.wi[i][j] = float(arr[n]) for i in xrange(self.nh): for j in xrange(self.no): n += 1 self.wo[i][j] = float(arr[n]) def update(self, inputs): if len(inputs) != self.ni-1: raise ValueError('wrong number of inputs') # input activations for i in range(self.ni-1): #self.ai[i] = sigmoid(inputs[i]) self.ai[i] = inputs[i] # hidden activations for j in range(self.nh): sum = 0.0 for i in range(self.ni): sum = sum + self.ai[i] * self.wi[i][j] self.ah[j] = sigmoid(sum) # output activations for k in range(self.no): sum = 0.0 for j in range(self.nh): sum = sum + self.ah[j] * self.wo[j][k] self.ao[k] = sigmoid(sum) return self.ao[:] def backPropagate(self, targets, N, M): if len(targets) != self.no: raise ValueError('wrong number of target values') # calculate error terms for output output_deltas = [0.0] * self.no for k in range(self.no): error = targets[k]-self.ao[k] output_deltas[k] = dsigmoid(self.ao[k]) * error # calculate error terms for hidden hidden_deltas = [0.0] * self.nh for j in range(self.nh): error = 0.0 for k in range(self.no): error = error + output_deltas[k]*self.wo[j][k] hidden_deltas[j] = dsigmoid(self.ah[j]) * error # update output weights for j in range(self.nh): for k in range(self.no): change = output_deltas[k]*self.ah[j] self.wo[j][k] = self.wo[j][k] + N*change + M*self.co[j][k] self.co[j][k] = change #print N*change, M*self.co[j][k] # update input weights for i in range(self.ni): for j in range(self.nh): change = hidden_deltas[j]*self.ai[i] self.wi[i][j] = self.wi[i][j] + N*change + M*self.ci[i][j] self.ci[i][j] = change # calculate error error = 0.0 for k in range(len(targets)): error = error + 0.5*(targets[k]-self.ao[k])**2 return error def test(self, patterns): for p in patterns: print(p[0], '->', self.update(p[0])) def weights(self): print('Input weights:') for i in range(self.ni): print(self.wi[i]) print() print('Output weights:') for j in range(self.nh): print(self.wo[j]) def train(self, patterns, iterations=1000, N=0.5, M=0.1): # N: learning rate # M: momentum factor for i in range(iterations): error = 0.0 for p in patterns: inputs = p[0] targets = p[1] self.update(inputs) error = error + self.backPropagate(targets, N, M) if i % 100 == 0: print('error %-.5f' % error) def demo(): # Teach network XOR function pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ] # create a network with two input, two hidden, and one output nodes n = NN(2, 2, 1) # train it with some patterns n.train(pat) # test it n.test(pat) n.save("z.txt") n.load("z.txt") n.test(pat) if __name__ == '__main__': demo()
0==False
Если я допустим хочу обучить сеть распознавать буквы алфавита и подам букву “А” в двоичном виде
“11100001”
как это представить программе?